Research On Route Planning Method Of Automatic Guide Vehicle For Logistics Warehousing

Automated Guided Vehicles(AGVs)are computer-controlled,driverless vehicles that follow guided paths to transport goods and materials between workstations.In recent years,multi-AGV systems have been widely used in logistics and warehousing systems such as courier sorting and automated containers.In the context of the increasing application of large AGV systems and the adoption of bidirectional paths to improve system flexibility,it has become increasingly important to consider spatial and temporal constraints into the system model to avoid collisions and improve system productivity,and the Time-space network model is a model that mainly consists of constraints to solve the path planning problem,so it is used as the basis for the path planning study in this thesis.In this thesis,the studied path planning problem is converted into a describable model,a Time-space network model is developed,and the hierarchical control structure of the Time-space network model is introduced.The model analyzes the optimization objectives of the studied problem and uses them as model variables and constraints to achieve the goal of reducing the task completion time by solving for a more reasonable path assignment.Based on the constructed Time-space network model,a model predictive control algorithm is introduced to optimize the Time-space network model using the rolling optimization technique it contains,adding some input parameters and decision variables to the Time-space network model and optimizing the original variables,and combining them with the Time-space network model to predict the guided vehicle based on the current state of the vehicle and the proposed future route using the dynamics model future route;calculate by minimizing the cost function to form the TSN-MPC hybrid model,and analyze the performance of the new hybrid model and the traditional Time-space network model in the path planning study through multiple sets of simulation experiments,and compare and analyze the advantages and disadvantages of the two models in terms of system task completion time and system energy consumption.The proposed task execution sequence strategy has improved the system energy saving effect by comparing and analyzing the TSN-MPC hybrid model and the traditional Time-space network model.Based on the single-vehicle single-task study,the path planning problem of one vehicle executing multiple tasks is considered and the model is extended,and the performance of the established model in the study of the sequential path planning problem with task execution is compared and analyzed with the traditional Time-space network model through a small-scale scenario example,which is tested and analyzed using computer numerical simulation experiments.The study shows that the hybrid TSN-MPC model proposed in this thesis solves the path planning problem and achieves a large improvement in three aspects,such as system task completion time,system energy consumption and solution time,which has certain theoretical significance and application value.